The practice of pharmacy has undergone radical change in recent years, with a paradigm shift from small, independent pharmacies to regional and national networks of Publicly-held Corporate Pharmacies (“PCPs”). The advent of PCPs was in response to a desire by the industry to minimize the cost of drug therapy while maximizing profitability. Under the PCP system, much of the decision-making power is shifted from health care providers, such as physicians, to an administrative organization that establishes standards of care, standardizes methods of delivering care, and evaluates the outcomes of the care given. PCPs work to minimize costs and maximize profits through a variety of means, including volume purchases, quality control, “formulary” lists of preferred medications, rebates for movement of market share, and negotiated medical fees.
As part of the PCPs' focus on reducing the cost of health care and maximizing profitability, there is a high degree of interest in acquiring as much historical and timely ongoing data as possible regarding drug use and benefit, comparative costs of alternate therapies, and patient demographics. This information is preferably collected, organized, and stored in a “data warehouse.” A data warehouse is a process by which large quantities of related data from many operational systems is merged into a single, standard repository to provide an integrated information view based on logical queries. Types of logical queries may relate to “data mining,” which can be defined as a process of data selection, exploration and building models using vast data stores to discover previously unknown patterns. Other queries may be in support of research on a particular subject, such as relationships between particular diseases and patient demographics. An accurate and timely data warehouse is a valuable tool that can provide information for use in a wide variety of therapeutic, statistical, and economic analyses and interventions to aid the PCP medical and business staffs in making health care and business related decisions. The data warehouse can also provide feedback regarding the impact of prior decisions on outcomes and profitability, facilitating improvements in patient care, operational efficiency, and reducing the cost of medical care. In the prior art, stored data from the various operational and software systems must be converted to the software operating system used by the data warehouse in order to make the data usable for queries. This conversion process can be time-consuming and expensive, and may lead to disposing of a great deal of partial data records, especially given the large quantity of non-standardized data handled by data warehouses.
Most PCPs are comprised of formerly independent pharmacies and franchises, many having varying proprietary systems and formats for keeping pharmacy records. When those pharmacies are merged into a larger pharmacy network, it is desirable in many cases to maintain these systems rather than replace them, due to the large expense and operational burden associated with purchasing and implementing a new system. A particular problem thus faced by PCPs is how to check records generated by the proprietary systems for validity, standardize them, and organize them for use in a data warehouse. If the data accumulated by the data warehouse is defective, untimely or incomplete, decisions based on the data may likewise be incorrect, incomplete, or untimely. This can cause the PCP to incur increased costs and have profits that are not maximized. Thus, there is an ongoing need for a method for efficiently collecting pharmacy records from various sources, resolving problems with faulty or incomplete records, and organizing the records to facilitate querying and analysis.